Security at airports and in other travel related areas is an important issue given today's sociopolitical climate, as well as other considerations. One technique used to promote travel safety is through baggage inspection. Often, an imaging apparatus is utilized to facilitate baggage screening. For example, an x-ray machine may be used to provide security personnel with a substantially two dimensional view of the contents of a bag and/or a computed axial tomography (CAT) device may be used to provide two and/or three dimensional views of objects. After viewing images provided by the imaging apparatus, security personnel may make a decision as to whether the baggage is safe to pass through the security check-point or if further (hands-on) inspection is warranted.
However, current screening techniques and systems utilize “bag-based” imaging apparatus, which merely screen one item at a time or need manual intervention by the screener to separate bags. That is, merely one bag can be run through an imaging apparatus before another bag can be put through the device. It can be appreciated that this “bag-based” imaging limits the number of items that can be imaged in a given period of time, thereby reducing the throughput at a security check-point.
Segmentation can be applied to baggage inspection in an attempt to increase the throughput at a security check-point. Segmentation essentially partitions a digital image into a plurality of regions (e.g., sets of pixels). However, segmentation techniques are typically limited to a particular application and/or a source that produces the image. Such techniques include, for example, “edge point grouping,” “region segmentation,” “merging,” and the “Hough transform” (see “Generalizing the Hough transform to detect arbitrary shapes,” Ballard D., 1987). Moreover, these techniques often create artificial shapes, and/or don't yield satisfactory results when objects in an image are close to one another (e.g., where multiple pieces of luggage are run through an imaging apparatus one right after another, with little to no space in-between). Further, region segmentation and/or merging techniques can be susceptible to image intensity variations, or changing object viewpoints. Additionally, the “Hough transform” can also be susceptible to image noise, can be limited to simple shapes, and can be computationally expensive.